A prenatal education interaction method based on a vocal music singing feedback training interface

Abstract

The application provides a kind of antenatal education interaction method based on vocal singing feedback training interface, the method is based on the user brain wave data set, the activity of different frequency bands is analyzed, the intensity of each wave band is identified and quantified, and the current emotional state of the user is judged by comparing with the preset threshold, and the time point of emotional change is recorded, the emotional analysis result is obtained, the rhythm, volume and spatial sound effect setting of music are adjusted, the dynamic adjustment of music output parameter is realized, the emotional intervention demand is optimized, and according to the change of user brain wave, the emotional improvement and user satisfaction are evaluated, the improvement effect of music intervention on user psychological state can be measured, the effectiveness of feedback closed loop is ensured, and more efficient, accurate and psychological demand meeting music feedback training effect is provided for user.

Description

Technical Field

[0001] This invention relates to the field of health care, and more specifically, to a prenatal education interactive method based on a vocal singing feedback training interface. Background Technology

[0002] Traditional interaction methods rely heavily on single physiological signals for emotion state recognition, failing to fully integrate the multi-frequency brainwave activity characteristics of users. This can lead to misjudgments or omissions in complex emotional states. Regarding music output, traditional methods rely on relatively fixed adjustments to playback parameters, failing to dynamically optimize playback rhythm, sound effects, and spatial sound distribution based on real-time user feedback. This results in emotional intervention effects that are difficult to meet individualized needs. In evaluating the effectiveness of emotional interventions, existing technologies often rely on simple satisfaction statistics based on user feedback questionnaires, lacking in-depth analysis of changes in user brainwave data before and after music adjustments, leading to insufficient quantitative assessment of intervention effects. This limits the application of existing technologies in complex emotion regulation scenarios, making it difficult to provide effective and efficient emotional intervention support. Summary of the Invention

[0003] To address the needs outlined in the background art, this invention provides a prenatal education interactive method based on a vocal singing feedback training interface, comprising the following steps:

[0004] S1: Based on the EEG acquisition device, the user's EEG data is collected, and environmental noise and non-target signals are removed through signal amplification and filtering to obtain the user's EEG dataset;

[0005] S2: Based on the user's EEG dataset, analyze the activity in different frequency bands, identify and quantify the intensity of each band, and determine the user's current emotional state by comparing it with a preset threshold. Record the time points of emotional changes to obtain the emotion analysis results.

[0006] S3: Based on the emotion analysis results, the system retrieves music tracks matching the emotional state from a preset music database, plays the music, and simultaneously displays EEG data and music playback status through an interactive interface. The system also receives user adjustments to the music playback, providing real-time music feedback.

[0007] S4: Based on the real-time music feedback display information, monitor the user's brainwave response in real time, adjust the music parameters, and adjust the spatial sound effect settings of the music according to the user's immediate brainwave feedback, generating music feedback adjustment information; S5: Based on the music feedback adjustment information, analyze the impact of music playback adjustment on the user's emotions according to the changes in brainwaves before and after music playback and adjustment, evaluate the degree of emotion improvement and the user's satisfaction index, and obtain the user experience evaluation result.

[0008] Furthermore: the user's EEG dataset includes the frequency distribution of alpha, beta, and gamma waves, amplitude information for each band, and the total power spectrum of the EEG; the emotion analysis results include the user's emotion state label, the timestamp corresponding to the emotion state, and the emotion intensity index; the real-time music feedback interaction information includes the music track ID that matches the emotional needs, the playback order of the tracks, and the predetermined playback duration of each track; the music feedback adjustment information includes the volume adjustment value, the rhythm adjustment frequency, the user's EEG response change data, and the immediate playback status of the adjusted music; and the user experience evaluation results include a comparison of the emotional state before and after the music intervention, the percentage of emotion improvement, and the user satisfaction score.

[0009] Further: Based on the EEG acquisition device, the user's EEG data is collected. Through signal amplification and filtering, environmental noise and non-target signals are removed to obtain the user's EEG dataset. The specific steps are as follows:

[0010] S101: Based on an electroencephalogram (EEG) acquisition device, this device records the user's brain activity in real time, including the frequency, amplitude, and spatial distribution of brain waves, through an EEG sensor connected to the user's head, to obtain raw EEG data;

[0011] S102: Based on the original EEG data, high-pass and low-pass filters are applied to process the EEG data. Signal amplification parameters and filtering frequency range are set to remove low-frequency environmental noise and high-frequency non-target signals, thereby optimizing the clarity and usability of the data and obtaining filtered EEG data.

[0012] S103: Based on the filtered EEG data, timestamp the EEG data, and store and back up the EEG data to obtain the user's EEG dataset.

[0013] Furthermore: Based on the user's EEG dataset, the process of analyzing differentiated frequency band activities, identifying and quantifying the intensity of each band, comparing it with a preset threshold to determine the user's current emotional state, and recording the time points of emotional changes to obtain the emotion analysis results is as follows:

[0014] S201: Based on the user's EEG dataset, each EEG frequency band is separated through frequency band analysis. The peak energy and average power of each frequency band are analyzed, and the activity intensity of each frequency band is evaluated to obtain the frequency band intensity analysis result; S202: Based on the frequency band intensity analysis result, it is compared with a preset threshold related to the emotional state. Through logical judgment, the current user's emotional state is identified, and the emotional intensity is evaluated based on the frequency band activity intensity to obtain the emotional state determination result;

[0015] S203: Based on the emotional state determination result, the time points of emotional changes are recorded by timestamp marking, and the emotional state at different time points is analyzed to monitor emotional fluctuations and trends, thereby obtaining the emotional analysis result.

[0016] The beneficial effects of this invention are as follows: Based on the user's EEG dataset, this method analyzes differentiated frequency band activities, identifies and quantifies the intensity of each band, and determines the user's current emotional state by comparing it with a preset threshold. It also records the time points of emotional changes to obtain emotional analysis results. By adjusting the rhythm, volume, and spatial sound effects of the music, dynamic adjustment of the music output parameters is achieved, optimizing the emotional intervention requirements. Furthermore, based on changes in the user's EEG, the improvement in emotional state and user satisfaction are evaluated. This method can measure the effect of music intervention on improving the user's psychological state, ensuring the effectiveness of the feedback loop and providing users with a more efficient, accurate, and psychologically satisfying music feedback training effect. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of the method of the present invention is shown.

[0019] Figure 2 A flowchart illustrating the method for obtaining sentiment analysis results in the present invention is shown.

[0020] Figure 3 The steps for obtaining real-time music feedback interaction information in this invention are illustrated. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in the present invention are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of the present invention. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the present invention. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0022] Furthermore, the embodiments described herein are merely some, not all, of the embodiments of the invention. The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that the term "comprising" will be used in the embodiments of the present invention to indicate the presence of a feature subsequently declared, but does not preclude the addition of other features. It should also be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of the present invention, it should also be noted that the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0024] The following is a detailed description of this case, in conjunction with the relevant accompanying drawings in the instruction manual.

[0025] The specific steps include:

[0026] S1: Based on the EEG acquisition device, the user's EEG data is collected, and environmental noise and non-target signals are removed through signal amplification and filtering to obtain the user's EEG dataset;

[0027] S2: Based on the user's EEG dataset, analyze the activity in different frequency bands, identify and quantify the intensity of each band, and determine the user's current emotional state by comparing it with a preset threshold. Record the time points of emotional changes to obtain the emotion analysis results.

[0028] S3: Based on the emotion analysis results, the system retrieves music tracks matching the emotional state from a preset music database, plays the music, and simultaneously displays EEG data and music playback status through an interactive interface. The system also receives user adjustments to the music playback, providing real-time music feedback.

[0029] S4: Based on the real-time music feedback display information, monitor the user's brainwave response in real time, adjust the music parameters, and adjust the spatial sound effect settings of the music according to the user's immediate brainwave feedback, generating music feedback adjustment information; S5: Based on the music feedback adjustment information, analyze the impact of music playback adjustment on the user's emotions according to the changes in brainwaves before and after music playback and adjustment, evaluate the degree of emotion improvement and the user's satisfaction index, and obtain the user experience evaluation result.

[0030] The user's EEG dataset includes the frequency distribution of alpha, beta, and gamma waves, amplitude information for each band, and the total power spectrum of the EEG. The emotion analysis results include the user's emotion state label, the timestamp corresponding to the emotion state, and the emotion intensity index. The real-time music feedback interaction information includes the music track ID that matches the emotional needs, the playback order of the tracks, and the predetermined playback duration of each track. The music feedback adjustment information includes the volume adjustment value, the rhythm adjustment frequency, the user's EEG response change data, and the real-time playback status of the adjusted music. The user experience evaluation results include a comparison of the emotional state before and after the music intervention, the percentage of emotion improvement, and the user satisfaction score.

[0031] The specific steps for acquiring a user's brainwave dataset using an EEG acquisition device, followed by signal amplification and filtering to remove environmental noise and non-target signals, are as follows:

[0032] S101: Based on an electroencephalogram (EEG) acquisition device, this device records the user's brain activity in real time, including the frequency, amplitude, and spatial distribution of brain waves, through an EEG sensor connected to the user's head, to obtain raw EEG data;

[0033] S102: Based on the original EEG data, high-pass and low-pass filters are applied to process the EEG data. Signal amplification parameters and filtering frequency range are set to remove low-frequency environmental noise and high-frequency non-target signals, thereby optimizing the clarity and usability of the data and obtaining filtered EEG data.

[0034] S103: Based on the filtered EEG data, timestamp the EEG data, and store and back up the EEG data to obtain the user's EEG dataset.

[0035] Based on the user's EEG dataset, the steps for analyzing differentiated frequency band activities, identifying and quantifying the intensity of each band, comparing it with preset thresholds to determine the user's current emotional state, and recording the time points of emotional changes to obtain the emotion analysis results are as follows:

[0036] S201: Based on the user's EEG dataset, each EEG frequency band is separated through frequency band analysis. The peak energy and average power of each frequency band are analyzed, and the activity intensity of each frequency band is evaluated to obtain the frequency band intensity analysis result; S202: Based on the frequency band intensity analysis result, it is compared with a preset threshold related to the emotional state. Through logical judgment, the current user's emotional state is identified, and the emotional intensity is evaluated based on the frequency band activity intensity to obtain the emotional state determination result;

[0037] S203: Based on the emotional state determination result, the time points of emotional changes are recorded by timestamp marking, and the emotional state at different time points is analyzed to monitor emotional fluctuations and trends, thereby obtaining the emotional analysis result.

[0038] Based on the emotion analysis results, the system retrieves music tracks matching the emotional state from a pre-set music database, adjusts the playback parameters of the tracks, creates a playback queue, and plays the music. The system simultaneously displays EEG data and music playback status through an interactive interface, and receives real-time music feedback from the user through the interface.

[0039] S301: Based on the sentiment analysis results, access the preset music database, filter music tracks that match the current user's emotional state through sentiment tags and sentiment intensity, and obtain a music track list;

[0040] Based on the sentiment analysis results, a pre-set music database is connected. By matching sentiment tags and sentiment intensities with track information in the database, which uses SQL or NoSQL technology to store a large amount of track information, each track is labeled with a sentiment category such as "happy" or "sad" and a sentiment intensity index. During the matching process, the user's sentiment state is read, and all music tracks in the database that match this sentiment are queried. Precise filtering is performed using database query languages ​​such as SQL's SELECT and WHERE clauses. The filtering conditions are optimized based on sentiment similarity and user historical preference algorithms. For example, if the user's sentiment tag is "happy," music with fast tempos or positive lyrics is prioritized. Sensitivity intensity is used to adjust the range of song selection to ensure that the selected music best matches the user's current sentiment state. Finally, a personalized music track list is obtained, providing users with a music experience that supports sentiment regulation.

[0041] S302: Based on the music track list, adjust the start and end points of each song, adjust the playback order, optimize the smoothness of music playback, and obtain the adjusted playback queue;

[0042] Based on the generated music track list, the start and end points of each song are adjusted. During the adjustment process, music editing software or specialized algorithms are used to analyze the structure of each song and identify appropriate entry and end points. For example, automatic music analysis technology is used to detect the beat and energy peaks of the songs to set the optimal start and end points for each song, ensuring that the songs can quickly resonate with the user. At the same time, the playback order is adjusted according to the user's emotional change patterns. Machine learning models are used to predict the user's emotional change trends, and the order of the playlist is adjusted according to the prediction results to make the track flow more natural and in line with the user's emotional fluctuations, generating an adjusted playback queue.

[0043] S303: Based on the adjusted playback queue, it synchronously displays EEG data and music playback status on the interactive display interface, receives playback adjustments made by the user through the interface, including volume adjustment and track switching, and obtains real-time music feedback interaction information;

[0044] Based on the adjusted playback queue, EEG graphics and music playback controls are simultaneously displayed on the interactive interface, allowing users to intuitively see the impact of music on their emotions. The interface design uses modern UI / UX design principles to ensure ease of operation, such as adjusting the volume via a slider and switching tracks by clicking a button. These interactive operations are implemented using front-end technologies such as JavaScript and HTML5, and communicate with the back-end server via WebAPI to ensure real-time response. All user interactions are recorded on the server, providing real-time music feedback interaction information.

[0045] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A prenatal education interactive method based on a vocal singing feedback training interface, characterized in that... The process includes the following steps: S1: Based on the EEG acquisition device, the user's EEG data is acquired, and through signal amplification and filtering, environmental noise and non-target signals are removed to obtain the user's EEG dataset; S2: Based on the user's EEG dataset, analyze the activity in different frequency bands, identify and quantify the intensity of each band, and determine the user's current emotional state by comparing it with a preset threshold. Record the time points of emotional changes to obtain the emotion analysis results. S3: Based on the emotion analysis results, the system retrieves music tracks matching the emotional state from a preset music database, plays the music, and simultaneously displays EEG data and music playback status through an interactive interface. The system also receives user adjustments to the music playback, providing real-time music feedback. S4: Based on the real-time music feedback display information, monitor the user's brainwave response in real time, adjust the music parameters, and adjust the spatial sound effect settings of the music according to the user's immediate brainwave feedback, generating music feedback adjustment information; S5: Based on the music feedback adjustment information, analyze the impact of music playback adjustment on the user's emotions according to the changes in brainwaves before and after music playback and adjustment, evaluate the degree of emotion improvement and the user's satisfaction index, and obtain the user experience evaluation result.

2. The prenatal education interactive method based on vocal singing feedback training interface according to claim 1, characterized in that... The user's EEG dataset includes the frequency distribution of alpha, beta, and gamma waves, amplitude information for each band, and the total power spectrum of the EEG. The emotion analysis results include the user's emotion state label, the timestamp corresponding to the emotion state, and the emotion intensity index. The real-time music feedback interaction information includes the music track ID that matches the emotional needs, the playback order of the tracks, and the predetermined playback duration of each track. The music feedback adjustment information includes the volume adjustment value, the rhythm adjustment frequency, the user's EEG response change data, and the real-time playback status of the adjusted music. The user experience evaluation results include a comparison of the emotional state before and after the music intervention, the percentage of emotion improvement, and the user satisfaction score.

3. The prenatal education interactive method based on vocal singing feedback training interface according to claim 1, characterized in that... The specific steps for acquiring a user's brainwave dataset using an EEG acquisition device, followed by signal amplification and filtering to remove environmental noise and non-target signals, are as follows: S101: Based on an electroencephalogram (EEG) acquisition device, this device records the user's brain activity in real time, including the frequency, amplitude, and spatial distribution of brain waves, through an EEG sensor connected to the user's head, to obtain raw EEG data; S102: Based on the original EEG data, high-pass and low-pass filters are applied to process the EEG data. Signal amplification parameters and filtering frequency range are set to remove low-frequency environmental noise and high-frequency non-target signals, thereby optimizing the clarity and usability of the data and obtaining filtered EEG data. S103: Based on the filtered EEG data, timestamp the EEG data, and store and back up the EEG data to obtain the user's EEG dataset.

4. The prenatal education interactive method based on vocal singing feedback training interface according to claim 1, characterized in that... Based on the user's EEG dataset, the following steps are taken to analyze differentiated frequency band activities, identify and quantify the intensity of each band, compare it with a preset threshold to determine the user's current emotional state, and record the time points of emotional changes to obtain the emotion analysis results: S201: Based on the user's EEG dataset, each EEG frequency band is separated through frequency band analysis, the peak energy and average power of each frequency band are analyzed, the activity intensity of each frequency band is evaluated, and the frequency band intensity analysis results are obtained; S202: Based on the frequency band intensity analysis results, compare them with preset thresholds related to emotional state, identify the current user's emotional state through logical judgment, and evaluate the emotional intensity based on the frequency band activity intensity to obtain the emotional state determination result; S203: Based on the emotional state determination result, the time points of emotional changes are recorded by timestamp marking, and the emotional state at different time points is analyzed to monitor emotional fluctuations and trends, thereby obtaining the emotional analysis result.

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